Generating additional slices based on data access frequency

ABSTRACT

A method for execution by a computing device of a dispersed storage network. The method begins by determining whether frequency of access to a set of encoded data slices exceeds a frequently accessed threshold. The method continues, when the frequency of access exceeds the frequently accessed threshold, by determining an access amount indicative of a degree that the frequency of access exceeds the frequently accessed threshold. The method continues by generating a number of additional encoded data slices and storing the number of additional encoded data slices in a number of additional storage units, wherein the set of storage units and the number of additional storage units produce an expanded set of storage units. The method continues by sending a plurality of data access requests to subsets of the expanded set of storage units in a distributed manner to improve processing efficiency of the plurality of data access requests.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/222,819,entitled “IDENTIFYING AN ENCODED DATA SLICE FOR REBUILDING”, filed Sep.24, 2015, which is hereby incorporated herein by reference in itsentirety and made part of the present U.S. Utility Patent Applicationfor all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

Over time, the rate at which data is accessed may change. Some of thedata may be accessed more frequently than other data or more frequentlythan anticipated when the data was originally created and/or stored.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of another specific example of anerror encoding function in accordance with the present invention;

FIG. 10 is a schematic block diagram of another example of dispersedstorage error encoding of data in accordance with the present invention;

FIG. 11 is a schematic block diagram of yet another specific example ofan error encoding function in accordance with the present invention;

FIG. 12 is a schematic block diagram of yet another example of dispersedstorage error encoding of data in accordance with the present invention;and

FIG. 13 is a logic flow diagram of generating additional encoded dataslices for a frequently accessed set of encoded data slices inaccordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 and 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data (e.g., data 40) as subsequently described withreference to one or more of FIGS. 3-8. In this example embodiment,computing device 16 functions as a dispersed storage processing agentfor computing device 14. In this role, computing device 16 dispersedstorage error encodes and decodes data on behalf of computing device 14.With the use of dispersed storage error encoding and decoding, the DSN10 is tolerant of a significant number of storage unit failures (thenumber of failures is based on parameters of the dispersed storage errorencoding function) without loss of data and without the need for aredundant or backup copies of the data. Further, the DSN 10 stores datafor an indefinite period of time without data loss and in a securemanner (e.g., the system is very resistant to unauthorized attempts ataccessing the data).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The managing unit 18 creates and stores user profile information (e.g.,an access control list (ACL)) in local memory and/or within memory ofthe DSN memory 22. The user profile information includes authenticationinformation, permissions, and/or the security parameters. The securityparameters may include encryption/decryption scheme, one or moreencryption keys, key generation scheme, and/or data encoding/decodingscheme.

The managing unit 18 creates billing information for a particular user,a user group, a vault access, public vault access, etc. For instance,the managing unit 18 tracks the number of times a user accesses anon-public vault and/or public vaults, which can be used to generate aper-access billing information. In another instance, the managing unit18 tracks the amount of data stored and/or retrieved by a user deviceand/or a user group, which can be used to generate a per-data-amountbilling information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment (i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of six and a decode threshold number of three.In this example, similar to the example in FIG. 5, a first data segmentis divided into twelve data blocks (D1-D12), however unlike the examplein FIG. 5, this example includes a sixth row being added to the encodingmatrix. Therefore, when multiplying the encoding matrix by the datamatrix, a coding matrix with six rows of coded data blocks is nowproduced, where the first row of X11-X14 corresponds to a first encodeddata slice (EDS 1_1), the second row of X21-X24 corresponds to a secondencoded data slice (EDS 2_1), the third row of X31-X34 corresponds to athird encoded data slice (EDS 3_1), the fourth row of X41-X44corresponds to a fourth encoded data slice (EDS 4_1), the fifth row ofX51-X54 corresponds to a fifth encoded data slice (EDS 5_1), and thesixth row of X61-X64 corresponds to a sixth encoded data slice (EDS6_1). Note the first number of the EDS designation corresponds to thepillar number and that the second number of the EDS designationcorresponds to the data segment number.

FIG. 10 is a schematic block diagram of another example of dispersedstorage error encoding of data. In the present example, CauchyReed-Solomon has been selected as the encoding function, the datasegmenting protocol is to divide the data object into fixed sized datasegments, and the per data segment encoding values include: a pillarwidth of 6 (instead of 5, thus creating an extra encoded data slice perset), a decode threshold of 3, a read threshold of 4, and a writethreshold of 4. In accordance with the data segmenting protocol, thecomputing device 12 or 16 divides the data (e.g., a file (e.g., text,video, audio, etc.), a data object, or other data arrangement) into aplurality of fixed sized data segments (e.g., 1 through Y of a fixedsize in range of Kilo-bytes to Tera-bytes or more). The number of datasegments created is dependent of the size of the data and the datasegmenting protocol.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thecomputing device 12 or 16 stores a first set of encoded data slices (EDS1_1-EDS 6_1) to the expanded set of storage units (e.g., SU #1-#6 36),stores a second set of encoded data slices (EDS 1_2-EDS 6_2) to theexpanded set of storage units (e.g., SU #1-#6 36), etc., up to storingan nth set of encoded data slices (EDS 1_Y-EDS 6_Y) to the expanded setof storage units (e.g., SU #1-#6 36). Note the additional encoded dataslices (e.g., EDS 6_1 through EDS 6_Y) are stored in an additionalstorage unit SU #6 36 (i.e., additional in comparison to the fivestorage units for a pillar width of five). Further note that althoughnot specifically shown, each encoded data slice also includes acorresponding slice name.

FIG. 11 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of seven and a decode threshold number ofthree. In this example, like in FIG. 5, a first data segment is dividedinto twelve data blocks (D1-D12), however unlike FIG. 5, this examplethe encoding matrix includes an additional sixth row and an additionalseventh row. Therefore, when multiplying the encoding matrix by the datamatrix, a coding matrix with seven rows of coded data blocks isproduced, where the first row of X11-X14 corresponds to a first encodeddata slice (EDS 1_1), the second row of X21-X24 corresponds to a secondencoded data slice (EDS 2_1), the third row of X31-X34 corresponds to athird encoded data slice (EDS 3_1), the fourth row of X41-X44corresponds to a fourth encoded data slice (EDS 4_1), the fifth row ofX51-X54 corresponds to a fifth encoded data slice (EDS 5_1), the sixthrow of X61-X64 corresponds to a sixth encoded data slice (EDS 6_1), andthe seventh row of X71-X74 corresponds to a seventh encoded data slice(EDS 7_1). Note the first number of the EDS designation corresponds tothe pillar width number and that the second number of the EDSdesignation corresponds to the data segment number.

FIG. 12 is a schematic block diagram of yet another example of dispersedstorage error encoding of data. In the present example, CauchyReed-Solomon has been selected as the encoding function, the datasegmenting protocol is to divide the data object into fixed sized datasegments, and the per data segment encoding values include: a pillarwidth of 7 (instead of 5, thus creating two extra encoded data slicesper set), a decode threshold of 3, a read threshold of 4, and a writethreshold of 3. In accordance with the data segmenting protocol, thecomputing device 12 or 16 divides the data (e.g., a file (e.g., text,video, audio, etc.), a data object, or other data arrangement) into aplurality of fixed sized data segments (e.g., 1 through Y of a fixedsize in range of Kilo-bytes to Tera-bytes or more). The number of datasegments created is dependent of the size of the data and the datasegmenting protocol.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thecomputing device 12 or 16 stores a first set of encoded data slices (EDS1_1-EDS 7_1) to the expanded set of storage units (e.g., SU #1-#7 36),stores a second set of encoded data slices (EDS 1_2-EDS 7_2) to theexpanded set of storage units (e.g., SU #1-#7 36), up to storing an nthset of encoded data slices (EDS 1_Y-EDS 7_Y) to the expanded set ofstorage units (e.g., SU #1-#7 36). Note the additional encoded dataslices (e.g., EDS 6_1-EDS 6_Y and EDS 7_1_-EDS 7_Y) are stored inadditional storage units SU #6 36 and SU#7 36, respectively. Furthernote that although not specifically shown, each encoded data slice alsoincludes a corresponding slice name.

FIG. 13 is a flowchart illustrating an example of generating additionalencoded data slices for a frequently accessed set of encoded data slicesthat includes step 120 where the computing device determines whether afrequency of access to a set of encoded data slices exceeds a frequentlyaccessed threshold. When the frequency of access exceeds the frequentlyaccessed threshold, the method continues at step 122 where the computingdevices determines an access amount indicative of a degree in which thefrequency of access exceeds the frequently accessed threshold. Forexample, when the frequency of access exceeds the frequently accessedthreshold, the computing device determines the access amount based onone or more of determining a volume of the plurality of data accessrequests, determining a rate of increase of the plurality of data accessrequests, and determining a cost associated with the expanding the setof storage units based on one or more of historical performance,bandwidth, and available storage.

The method continues at step 124 where the computing device generates anumber of additional encoded data slices for the set based on the accessamount. For example, the computing device generates one additionalencoded data slice when the frequency of access exceeds the threshold by10%-20% and the cost analysis supports the expansion. As anotherexample, the computing device generates two additional encoded dataslices when the frequency of access exceeds the threshold by 20%-40% andthe cost analysis supports the expansion. As yet another example, thecomputing device generates three additional encoded data slices when thefrequency of access exceeds the threshold by 40% or more and the costanalysis supports the expansion.

The method continues with step 126 where the computing device stores thenumber of additional encoded data slices in a number of additionalstorage units. Note the set of storage units and the number ofadditional storage units produce an expanded set of storage units. Themethod continues with step 128 where the computing device sends dataaccess requests for the set of encoded data slices to subsets of theexpanded set of storage units in a distributed manner. Since a read anda write threshold are less than the pillar width number, a plurality ofdata access requests can be distributed among the storage units toeffectively load balance the requests among all of the storage units inthe set.

For example, and assuming a read/write threshold of 4 and a pillar widthnumber of 6, a first request is sent to storage units 1-4, a secondrequest is sent to storage units 2-5, a third request is sent to storageunits 3-6, a fourth request is sent to storage units 1 and 4-6, a fifthrequest is sent to storage units 1, 2, 5, and 6, and so. Thus, overtime, each storage unit receives about an equal number of requests, butless than all of the requests. Note if the read threshold is less thanor equal to ½*pillar width number, then two read requests may beperformed in parallel. For example, and assuming a read/write thresholdof 4 and a pillar width number of 8, a first read request is sent tostorage units 1-4 substantially concurrently with sending a secondrequest to storage units 5-8.

The method continues at step 130 where the computing device determineswhether the rate of the frequency of access has changed. If not, themethod loops back to step 130. When the rate of the frequency of accessis increasing, the method loops back to step 122 for the higher rate ofthe frequency access. When the rate of the frequency of access isdecreasing, the method continues to step 132 where the computing devicedetermines whether to delete one or more of the additional encoded dataslices. For example, when the frequency of access is decreasing, thecomputing device determines a rate of decreasing and, based on the rateof decreasing, determines whether one or more of the additional encodeddata slices are to be deleted. When the one or more of the additionalencoded data slices are not to be deleted, the method loops back to step130.

When the one or more of the additional encoded data slices are to bedeleted, the method continues to step 134 where the computing devicedeletes one or more of the additional encoded data slices. When theadditional encoded data slices have been deleted, the method continuesat step 120 where the computing device determines whether the frequencyof access is above the frequently access threshold. If yes, the methodcontinues at step 122. If not, the method continues at step 136 wherethe computing device determines whether frequency of access has droppedbelow a second frequency access threshold, which is less than thefrequency access threshold. If not, the method loops back to step 120.

When the frequency of access is below the second frequently accessedthreshold, the method continues to step 138, where the computing devicedetermines whether to delete one or more encoded data slices. Forexample, and assuming a read/write threshold of 4, a pillar width numberof 5, and two additional encoded data slices have been created, thecomputing device determines whether to delete one or both of theadditional encoded data slices. The computing device may further decideto delete all of the additional encoded data slices and one more encodeddata slice, leaving only four encoded data slices when the frequency ofaccess is well below the second threshold.

If computing device determines not to delete an encoded data slice, themethod loops back to step 120. When the computing device determines todelete an encoded data slice, the method continues to step 140 where thecomputing device deletes an encoded data slice(s). The method thencontinues back to step 120.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a computing device of adispersed storage network (DSN), the method comprises: determiningwhether a frequency of access via the DSN from one or more othercomputing devices to a set of encoded data slices that is stored in aset of storage units of the DSN exceeds a frequently accessed threshold,wherein a data segment of a data object is dispersed storage errorencoded in accordance with first dispersed error encoding parametersincluding a first encoding matrix to produce the set of encoded dataslices that is stored in the set of storage units of the DSN, whereinthe set of encoded data slices includes a pillar width number and adecode threshold number, wherein the pillar width number corresponds tonumber of encoded data slices in the set of encoded data slices, andwherein the decode threshold number corresponds to a number of encodeddata slices of the set of encoded data slices to retrieve acorresponding data segment of the data object; when the frequency ofaccess via the DSN from the one or more other computing devices to theset of encoded data slices that is stored in the set of storage units ofthe DSN exceeds the frequently accessed threshold, determining an accessamount indicative of a degree in which the frequency of access exceedsthe frequently accessed threshold; generating a number of additionalencoded data slices for the set of encoded data slices based on theaccess amount in accordance with second dispersed error encodingparameters including a second encoding matrix that includes at least oneof more rows or more columns than the first encoding matrix; storing thenumber of additional encoded data slices in a number of additionalstorage units within the DSN, wherein the set of storage units and thenumber of additional storage units produce an expanded set of storageunits within the DSN that includes more storage units than the set ofstorage units; and sending, via the DSN from at least one of thecomputing device or the one or more other computing devices, a pluralityof data access requests for the set of encoded data slices to differentrespective subsets of the expanded set of storage units in a distributedmanner to load balance the plurality of data access requests for the setof encoded data slices among the expanded set of storage units withinthe DSN, wherein, over time, each storage unit of the expanded set ofstorage units within the DSN receives approximately an equal number ofthe plurality of data access requests and less than all of the pluralityof data access requests.
 2. The method of claim 1 further comprises:when the frequency of access has exceeded the frequently accessedthreshold and when the frequency of access is decreasing, determining arate of the decreasing; based on the rate of decreasing, determiningwhether one or more of the additional encoded data slices are to bedeleted; and when the one or more of the additional encoded data slicesare to be deleted, deleting the one or more of the additional encodeddata slices.
 3. The method of claim 2 further comprises: when theadditional encoded data slices have been deleted, determining whetherthe frequency of access is below a second frequently accessed threshold,wherein the second frequently accessed threshold is less than thefrequently accessed threshold; when the frequency of access is below thesecond frequently accessed threshold, determining whether to delete oneor more encoded data slices of the set of encoded data slices; and whenthe one or more encoded data slices of the set of encoded data slices isto be deleted, deleting the one or more encoded data slices of the setof encoded data slices.
 4. The method of claim 1 further comprises:determining whether the frequency of access is below a second frequentlyaccessed threshold, wherein the second frequently accessed threshold isless than the frequently accessed threshold; when the frequency ofaccess is below the second frequently accessed threshold, determiningwhether to delete one or more encoded data slices of the set of encodeddata slices; and when the one or more encoded data slices of the set ofencoded data slices is to be deleted, deleting the one or more encodeddata slices of the set of encoded data slices.
 5. The method of claim 1,wherein the sending the plurality of data access requests for the set ofencoded data slices to the subsets of the expanded set of storage unitsfurther comprises: sending a first data access request of the pluralityof data access requests to a first subset of the expanded set of storageunits; and sending, substantially concurrently with the first dataaccess request, a second data access request of the plurality of dataaccess requests to a second subset of the expanded set of storage unitswhen an expanded pillar width number is equal to or greater than twice aread threshold number, wherein the expanded pillar width numbercorresponds to the expanded set of storage units.
 6. The method of claim1, wherein the generating the number of additional encoded data slicesfurther comprises: determining a cost associated with the expanded setof storage units; when the cost is below a first cost threshold,generating a first number of additional encoded data slices; when thecost is equal to or above the first cost threshold, generating a secondnumber of additional encoded data slices, wherein the second number isless than the first number, and wherein the cost associated with theexpanded set of storage units includes on at least one of: historicalperformance; bandwidth; or available storage.
 7. The method of claim 1,wherein the determining the access amount comprises one or more of:determining a volume of the plurality of data access requests;determining a rate of increase of the plurality of data access requests;and determining that the frequency of access has exceeded a secondfrequently accessed threshold, which is greater than the frequentlyaccessed threshold.
 8. A computing device comprises: an interfaceconfigured to interface and communicate with a dispersed storage network(DSN); memory; and a processing module operably coupled to the memoryand the interface, wherein the processing module is operable to:determine whether a frequency of access via the DSN from one or moreother computing devices to a set of encoded data slices that is storedin a set of storage units of the DSN exceeds a frequently accessedthreshold, wherein a data segment of a data object is dispersed storageerror encoded in accordance with first dispersed error encodingparameters including a first encoding matrix to produce the set ofencoded data slices that is stored in the set of storage units of theDSN, wherein the set of encoded data slices includes a pillar widthnumber and a decode threshold number, wherein the pillar width numbercorresponds to number of encoded data slices in the set of encoded dataslices, and wherein the decode threshold number corresponds to a numberof encoded data slices of the set of encoded data slices to retrieve acorresponding data segment of the data object; when the frequency ofaccess via the DSN from the one or more other computing devices to theset of encoded data slices that is stored in the set of storage units ofthe DSN exceeds the frequently accessed threshold, determine an accessamount indicative of a degree in which the frequency of access exceedsthe frequently accessed threshold; generate a number of additionalencoded data slices for the set of encoded data slices based on theaccess amount in accordance with second dispersed error encodingparameters including a second encoding matrix that includes at least oneof more rows or more columns than the first encoding matrix; store thenumber of additional encoded data slices in a number of additionalstorage units within the DSN, wherein the set of storage units and thenumber of additional storage units produce an expanded set of storageunits within the DSN; DSN that includes more storage units than the setof storage units; and send, via the interface and via the DSN from atleast one of the computing device or the one or more other computingdevices, a plurality of data access requests for the set of encoded dataslices to different respective subsets of the expanded set of storageunits in a distributed manner to load balance the plurality of dataaccess requests for the set of encoded data slices among the expandedset of storage units within the DSN, wherein, over time, each storageunit of the expanded set of storage units within the DSN receivesapproximately an equal number of the plurality of data access requestsand less than all of the plurality of data access requests.
 9. Thecomputing device of claim 8, wherein the processing module is furtheroperable to: when the frequency of access has exceeded the frequentlyaccessed threshold and when the frequency of access is decreasing,determine a rate of the decreasing; based on the rate of decreasing,determine whether one or more of the additional encoded data slices areto be deleted; and when the one or more of the additional encoded dataslices are to be deleted, delete one or more of the additional encodeddata slices.
 10. The computing device of claim 9, wherein the processingmodule is further operable to: when the additional encoded data sliceshave been deleted, determine whether the frequency of access is below asecond frequently accessed threshold, wherein the second frequentlyaccessed threshold is less than the frequently accessed threshold; whenthe frequency of access is below the second frequently accessedthreshold, determine whether to delete one or more encoded data slicesof the set of encoded data slices; and when the one or more encoded dataslices of the set of encoded data slices is to be deleted, delete theone or more encoded data slices of the set of encoded data slices. 11.The computing device of claim 8, wherein the processing module isfurther operable to: determine whether the frequency of access is belowa second frequently accessed threshold, wherein the second frequentlyaccessed threshold is less than the frequently accessed threshold; whenthe frequency of access is below the second frequently accessedthreshold, determine whether to delete one or more encoded data slicesof the set of encoded data slices; and when the one or more encoded dataslices of the set of encoded data slices is to be deleted, delete theone or more encoded data slices of the set of encoded data slices. 12.The computing device of claim 8, wherein the processing module isfurther operable to send, via the interface, the plurality of dataaccess requests for the set of encoded data slices to the subsets of theexpanded set of storage units by: sending, via the interface, a firstdata access request of the plurality of data access requests to a firstsubset of the expanded set of storage units; and sending, via theinterface, substantially concurrently with the first data accessrequest, a second data access request of the plurality of data accessrequests to a second subset of the expanded set of storage units when anexpanded pillar width number is equal to or greater than twice a readthreshold number, wherein the expanded pillar width number correspondsto the expanded set of storage units.
 13. The computing device of claim8, wherein the processing module is further operable to generate thenumber of additional encoded data slices by: determining a costassociated with the expanded set of storage units; when the cost isbelow a first cost threshold, generating a first number of additionalencoded data slices; when the cost is equal to or above the first costthreshold, generating a second number of additional encoded data slices,wherein the second number is less than the first number, and wherein thecost associated with the expanded set of storage units includes on atleast one of: historical performance; bandwidth; or available storage.14. The computing device of claim 8, wherein the processing module isfurther operable to determine the access amount by: determining a volumeof the plurality of data access requests; determining a rate of increaseof the plurality of data access requests; and determining that thefrequency of access has exceeded a second frequently accessed threshold,which is greater than the frequently accessed threshold.